The lack of rigorous non-parametric statistical tests of confoudner-effects significantly hampers the development of
robust, valid and generalizable predictive models in many fields of research.
mlconfound implements the partial and full confounder tests , that build on a recent theoretical framework of conditional
independence testing  and test the null hypothesis of no bias and fully biased model, respectively.
The proposed tests set no assumptions about the distribution of the predictive model output that is often non-normal.
As shown by theory and simulations, the test are statistically valid, robust and display a high statistical power.
 T. Spisak, Statistical quantification of confounding bias in predictive modelling, preprint on
arXiv:2111.00814 <http://arxiv-export-lb.library.cornell.edu/abs/2111.00814>_, 2021.
 Berrett, T. B., Wang, Y., Barber, R. F., and Samworth, R. J. (2020). The conditional permutation test for independencewhile controlling for confounders.Journal of the Royal Statistical Society: Series B (Statistical Methodology),82(1):175–197.
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